{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DS2000 Rigol Waveform Examples\n",
    "\n",
    "**Scott Prahl**\n",
    "\n",
    "**March 2021**\n",
    "\n",
    "This notebook demonstrates extracting signals from `.wfm` files created by Rigol DS2000 oscilloscopes.  It also validates that the process works by comparing with `.csv` and screenshots.\n",
    "\n",
    "*If RigolWFM is not installed, uncomment the following cell (i.e., delete the #) and run (shift-enter)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --user RigolWFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "try:\n",
    "    import RigolWFM.wfm as rigol\n",
    "except ModuleNotFoundError:\n",
    "    print('RigolWFM not installed. To install, uncomment and run the cell above.')\n",
    "    print('Once installation is successful, rerun this cell again.')\n",
    "\n",
    "repo = \"https://github.com/scottprahl/RigolWFM/raw/master/wfm/\"\n",
    "\n",
    "def read_rigol_csv(name):\n",
    "    csv_data = np.genfromtxt(name, delimiter=',', skip_header=2).T\n",
    "    lines = len(csv_data[0])\n",
    "    offset, scale = np.genfromtxt(name, delimiter=',', skip_header=1, skip_footer=lines, usecols=(3, 4)).T\n",
    "    csv_data[0] = offset + scale * csv_data[0]\n",
    "    return csv_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of Rigol scopes in the DS2000 family is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2', '2000', 'DS2000', 'DS2072A', 'DS2102A', 'MSO2102A', 'MSO2102A-S', 'DS2202A', 'MSO2202A', 'MSO2202A-S', 'DS2302A', 'MSO2302A', 'MSO2302A-S']\n"
     ]
    }
   ],
   "source": [
    "print(rigol.DS2000_scopes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS2000-A\n",
    "\n",
    "### Look at a screen shot\n",
    "\n",
    "Start with a `.wfm` file from a Rigol DS2000 scope.  It should look something like this\n",
    "\n",
    "<img src=\"https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2000-A.png\" width=\"40%\">\n",
    "\n",
    "### Look at the data in the `.csv` file\n",
    "\n",
    "First let's look at plot of the data from the corresponding `.csv` file. Unfortunately this wfm file did not come\n",
    "with a `.csv` file generated by the Rigol oscilloscope.  Instead, the `.csv` was generated by RigolWFM and\n",
    "verifies that the `.csv` generation matches that expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"DS2000-A.csv\"\n",
    "\n",
    "csv_data = np.genfromtxt(repo+filename, delimiter=',', skip_header=2).T\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(csv_data[0]*1e6,csv_data[1], color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.ylim(-10,10)\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(csv_data[0]*1e6,csv_data[2], color='red')\n",
    "plt.xlabel(\"Time (µs)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.grid(True)\n",
    "plt.ylim(-4,4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now for the `.wfm` data\n",
    "\n",
    "First a textual description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading 'https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2000-A.wfm?raw=true'\n",
      "    General:\n",
      "        File Model   = wfm2000\n",
      "        User Model   = 2000\n",
      "        Parser Model = wfm2000\n",
      "        Firmware     = 00.03.00.01.03\n",
      "        Filename     = DS2000-A.wfm\n",
      "        Channels     = [1, 2]\n",
      "\n",
      "     Channel 1:\n",
      "         Coupling =       DC\n",
      "            Scale =     5.00  V/div\n",
      "           Offset =     0.00  V\n",
      "            Probe =      10X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =   50.000 µs/div\n",
      "           Offset = -199.200 µs\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =   700000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...    699999,   700000]\n",
      "           Raw    = [      128,      127,      129  ...       128,      128]\n",
      "           Times  = [-549.200 µs,-549.199 µs,-549.198 µs  ... 150.799 µs,150.800 µs]\n",
      "           Volts  = [200.00 mV, -0.00  V,400.00 mV  ... 200.00 mV,200.00 mV]\n",
      "\n",
      "     Channel 2:\n",
      "         Coupling =       DC\n",
      "            Scale =     2.00  V/div\n",
      "           Offset =    -6.00  V\n",
      "            Probe =      10X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =   50.000 µs/div\n",
      "           Offset = -199.200 µs\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =   700000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...    699999,   700000]\n",
      "           Raw    = [       53,       52,       53  ...        52,       53]\n",
      "           Times  = [-549.200 µs,-549.199 µs,-549.198 µs  ... 150.799 µs,150.800 µs]\n",
      "           Volts  = [ 80.00 mV,476.84 nV, 80.00 mV  ... 476.84 nV, 80.00 mV]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# raw=true is needed because this is a binary file\n",
    "wfm_url = repo + \"DS2000-A.wfm\" + \"?raw=true\"  \n",
    "w = rigol.Wfm.from_url(wfm_url, '2000')\n",
    "\n",
    "description = w.describe()\n",
    "print(description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O2+Ewdtt4t0zLbFYzAEtjaablHrzdwQD814H/lWm5zWrOPNa8NCn5GQTZXqHyi8d/AcClj1yaablCpYOmTN027TaefvPpTMsUyRVFWa/bUtvbRz70kUzLlZTLdtu6tHXZbzgrUrJu8/qd1Wubw0DgT+nC9wFujIi7iw2pdktjaeYbQmkHtiSWZFru6s2rA7D1eltnWm6TmjKPFeAz236G9xe9n2mZpR1Y1j+yE3c9kTHPjeGHn/hhpuVKynxnm5fSDizrZDbngzlA9g3TIp91e/eM7HdfeSXekrpMDhHxEjC46Di6akksWbYzz0pzU5JslizNdodb2oFnncya1Zx5rJDswLNet3ntwNZafS2ALl+D3p68zhzyUNqBZe2ihy4C4IonruBzO30us3Kb1NQ46zan7bZkpZJDWsWzMCKHQ8JVyJKlS5btzLOSV7VSqbzM423Kp1opl+SQ0xFYafmzjrdJTbms230/vG9uO/Os123r0lYAVmtaLdNy86pWykNpu8or3g63WklNko6XdKekt4DngdclTZF0maRs7xpaRSyJJZkfiZc8+caTmZZXOrrPegd270v38vq81zMtE/JJDsvaHDI+AiuVl8eZTh5VCRHRMGdlp/3LaQB8b6/vZVpuXtVKe262Jwdue2CmZeZdrdTZljAO2BY4G9g4IraIiI2AfYDHgEskfSmXyBrYX//+Vx5+9eFMyyzdbfvrib/OtNy8qpVm/nNmpuWV5FmtlNdZWR5nOnlUJTTSWdmAvgMAWLvv2pmWm+e6LW1nWSm6WumAiFjctmdEzAFuAW6RlO15nVWV9Z3RJbPfnw3A3JZsH6Ox+ya7M+n1SZmWCcmPLOt1kXe1Uh47hTyOFvPcgWVt2brNuBqsSU35nJWR/VlZXlfZLSu/k+G3SjpBUv/2RqiWPCx7ffv0zaXcCx68AIDRz4zOtNxPbPmJzI/qoLEapEs/2kY5c8hjB7as7AaqssurrSzrRJbXVXYlna3Zq4FDgZck3SzpKEmr5xLJKmTYZsMyr1/MuuGtpPTjyjr55HXVRyNVfeRWrZTjmUOvX7d5Jd6IhqtW6nDNRsRtEXEcsBVJNdKJwCuSfivp07lEtArIY0Mo3Y+QtZGfGAnAN4dk+0ROkc8R2GOzHuOeF+/JtMy8GqTzqvrItV4861jzrlbKuPxGqlYqukE6mXnEgoj4Q0QcRXK38m5Aw9yU1tPy2BDySg79V09qDNfos0am5eb1I8tDXg3SvlppxbIzLS+vKrsGqlbK66BmWfm1jCRpoKR/lfQwcCswFtg9l4hWAXlsCKs151Ot1Gg/MoB+q/XLtDxXfSQasVqpUc7K8rxMuJDHZ0j6BnAcsCNJtdJ/RMQjuUSyCsmjWimvNodcr/rI4Uc2aMNBuTxPB3KsVvLVSg3VIN0w67bgx2fsBVwE/CWiQW4brANBZL6zzataqfQjy2PDzeOIJo9Hk+T1I3vureRZkf9c+M9My22kq5XyuuM6z7vPG2bdFtkgDZwXEfe2lxiU2DyHuBpaLmcOOVcrNcr14o10h3TpqawPvvxgpuU20tVKJQ1zD0lOBzW5tjkU1CB9maRbJJ0oaZCkjSRtKWl/SecDDwPZvjV8FZDHUUJej+PI68whr+f/LI2lmT8HKq+623OHnwvAMYOOybTcRrpaqXQH/q3P35ppuXm1lV0z6RreW5T9+6lzuZQ15/scOqxWiohjJO0MnAB8DdgEWABMBf4MXBARC3OJrIE10iWBjdYgvWRp41QrlRrO+zZnew9JI12tVHq8+v0v359puXm1leX1bupVsVqJiJgSESMjYnhE7BgRH4uI4yNitBNDdXkcJeQlzwZpyKeRN6+XpmQda+nJoVmf6Vz75LUsbM3+p5dHtdIWa28BwHeGfifTcvNqkM7L9HemM+a5MZmWWfrt3vTcTZmWW9IYa7bOzX5/Nk3nNvHQKw8x4KIBTP7H5Nwa4rKWZ4M0ZH80nkeDdKm8X038VabllqpUsn4WVOlFN50ZdOUgLnnokprLnfbONP4w+Q9dDauq0rJvuNaGmZabV4P053f6fLemP/ymwznt7tMyiqZjpQOEia9PzKV81euLLSQdBPwMaAZ+ExEXtzfu0KFDY8KECSs9j1um3MLR/3N014PsRJyT7brVufklnL99/W8M22xYZuXlGStku24jgqbz8jtOeu17r7HJgE0yK6+R1i3kG+/0U6ez/QbbZ1ZeI63b1qWtrHb+anxryLe46tCrulSGpIkRMbTasLo8c5DUDFwBHAzsDByXtn1kKs/E0GhenPNi0SEUJu8qwFL1kmXvrflvFR1CYUrVq1mf8ZbUeof03ulb4JD0JUk/lfThXCJKDANmRMRLEdECjAGOyHF+vV7Wr7K05WqtBrKV15u329JBzcb9N86l/ForQ68CBksaDHwf+A1wPfDJXKKCzYBXy77PAvYsH0HSCGAEwMCBAxk/fvxKz2T7/tvzwrwXuh5lB07Z9pQuxVSUppebGP/K+KLDqMmVH7uyodbtu8+/y/jnx2dW3lb9tmLmgpmZlVfugkEXZL5uPzLgIzw/9/lMyyyZ/exsZjM7s/JGDxvNlx7P5/1lF+5yYebr9rxB57Fj/x3z+T1ERKcdMCn9+yPg5PJ+eXTA0STtDKXvXwZ+2d74Q4YMia5oaW2J9S5eL+6YdseyfldPuDr+89H/XPb9rhfuihnvzOhS+Vnb9apdg1FE87nNcda9Z8Wpd54as96bVXRYVc1+f3YwijjjnjPirHvPih/f/+OY9NqkZcMnvzU5/vrSX5d9f+391+KWKbcUEWpERAy7Zlgwihh0xaBgFLH3tXsHo4gf3PeDWLxkcVwz8ZpoXdIaS5cujd8++duY3zK/sFhbWlti48s3jqNvPjrmLJgTE1+bGCf+6cR46vWn4oGZDxQW16ri+qeuj4sfvDgiknV90q0nxcTXJq4wzri/j4vJb00uIrxMAROinf1qTQ3Sku4neQrrScC+wFvA0xHx0ayTVTq/vYBREXFg+v1sgIi4qNr4XW2QNjPrzbJokP4isIjkrOENYHPgsoziq+YJYHtJW6cvFzoWuD3H+ZmZWZla2xxOi4gzS18i4hVJg3KKiYholXQqyaPBm4HrImJyXvMzM7MV1XrmUO2tbwdnGUhbEfHniNghIraNiAvynJeZma2os/c5fBv4DrCNpGfKBg0A/F4HM7NVVGfVSjcCd5G80+Gssv5zI8IXb5uZraI6Sw7NwPvAKW0HSFrfCcLMbNXUWXKYCMuenNb2GQMBbJN5RGZmVrjO3uewdU8FYmZm9aPmZwlLOpzkBjiA8RFxRz4hmZlZ0Wp98N7FwHeBKWn3XUkX5hmYmZkVp9Yzh0OA3SKSN2xI+j3wJPCDvAIzM7PirMz7HNYt+7xOxnGYmVkdqfXM4SLgSUnjSK5a2pcV73swM7NVSGd3SF8B3BgRN0kaD+yRDjozfQCfmZmtgjo7c5gOXC5pE+Bm4KaIeDL/sMzMrEgdtjlExM8iYi+SN769A1wn6XlJ50jaoUciNDOzHldTg3REvBwRl0TEx4DjgCOBqXkGZmZmxan1Poc+kg6TdAPJg/imAZ/LNTIzMytMh8lB0qclXQfMAr4B3AlsGxHHRsRteQQkaZSk2ZKeSrtD8piPmZm1r7MG6bNJHtv9/Yh4twfiKfnPiLi8B+dnZmZlOnvw3v49FYiZmdWPmh+818NOlXQiMIF2zlokjQBGAAwcOJDx48f3bIRmZqswRUTnY2U9U+k+YOMqg0YCjwFvk7wv4nxgk4j4WkflDR06NCZMmJB5nGZmqzJJEyNiaLVhhZw5RMQBtYwn6RrAjwY3M+thK/PgvR6R3o1dchTwXFGxmJn1VvXY5nCppN1IqpVmAt8sNBozs16o7pJDRHy56BjMzHq7uqtWMjOz4jk5mJlZBScHMzOr4ORgZmYVnBzMzKyCk4OZmVVwcjAzswpODmZmVsHJwczMKjg5mJlZBScHMzOr4ORgZmYVnBzMzKyCk4OZmVVwcjAzswpODmZmVqGQ5CDpGEmTJS2VNLTNsLMlzZA0TdKBRcRnZtbbFfUmuOeAzwG/Lu8paWfgWGAQsClwn6QdImJJz4doZtZ7FXLmEBFTI2JalUFHAGMiYlFE/B2YAQzr2ejMzKze3iG9GfBY2fdZab8KkkYAI9Kv8yRVSzZmZta+D7c3ILfkIOk+YOMqg0ZGxG3dLT8irgau7m45ZmZWKbfkEBEHdGGy2cAWZd83T/uZmVkPqrdLWW8HjpXUV9LWwPbA4wXHZGbW6xR1KetRkmYBewF3ShoLEBGTgZuBKcDdwCm+UsnMrOcpIoqOwczM6ky9VSuZmVkdcHIwM7MKTg5mZlbBycHMzCo4OZiZWQUnBzMzq+DkYGZmFZwczMysgpODmZlVcHIwM7MKTg5mZlahbpODpGZJT0q6o+hYzMx6m7pNDsB3galFB2Fm1hvVZXKQtDnwWeA3RcdiZtYb1ds7pEv+CzgDGNDeCOXvkF5zzTWHbLHFFu2NWoilS5fS1FSXubdCI8UKjRVvI8UKjRVvI8UK9Rnv9OnT346IDasOjIi66oBDgSvTz8OBOzqbZsiQIVFvxo0bV3QINWukWCMaK95GijWiseJtpFgj6jNeYEK0s1+trzSW2Bs4XNJMYAywv6TRxYZkZta71F1yiIizI2LziNgKOBb4a0R8qeCwzMx6lbpLDmZmVrx6bZAGICLGA+MLDsPMrNfxmYOZmVVwcjAzswpODmZmVsHJwczMKjg5mJlZBScHMzOr4ORgZmYVnBzMzKyCk4OZmVVwcjAzswpODmZmVsHJwczMKjg5mJlZBScHMzOr4ORgZmYVnBzMzKxCXSYHSVtIGidpiqTJkr5bdExmZr1Jvb4JrhX4fkRMkjQAmCjp3oiYUnRgZma9QV2eOUTE6xExKf08F5gKbFZsVGZmvYciougYOiRpK+ABYJeIeL+s/whgBMDAgQOHjBkzppgA2zFv3jz69+9fdBg1aaRYobHibaRYobHibaRYoT7j3W+//SZGxNCqAyOibjugPzAR+FxH4w0ZMiTqzbhx44oOoWaNFGtEY8XbSLFGNFa8jRRrRH3GC0yIdvardVmtBCBpNeAW4IaI+GPR8ZiZ9SZ1mRwkCbgWmBoRPy06HjOz3qYukwOwN/BlYH9JT6XdIUUHZWbWW2RyKauktYCFEbEki/Ii4iFAWZRlZmYrr0tnDpKaJB0v6U5JbwHPA6+nN61dJmm7bMM0M7Oe1NVqpXHAtsDZwMYRsUVEbATsAzwGXCLpSxnFaGZmPayr1UoHRMTitj0jYg7JFUa3pFcbmZlZA+rqmcOtkk6Q1O4dHdWSh5mZNYauJoergUOBlyTdLOkoSatnGJeZmRWoS8khIm6LiOOArUiqkU4EXpH0W0mfzjA+MzMrQLfuc4iIBRHxh4g4CvgMsBtwdxaBmZlZcbqVHCQNlPSvkh4GbgXGArtnEZiZmRWnS1crSfoGcBywI0m10n9ExCNZBmZmZsXp6qWsewEXAX+JiKUZxmNmZnWgq8nhvIiY2d7A9MF5m0XErC6Wb2ZmBepqcrhMUhNwG8n7Fv4BrAFsB+wHfAo4B3ByMDNrQF1KDhFxjKSdgROArwGbAAtIXuf5Z+CCiFiYWZRmZtajuvxU1oiYAozMMBYzM6sT9fo+BzMzK1DdJgdJB0maJmmGpLOKjsc68eab8OKLRUdhZhlR8o7p+iKpGZgOfJqkUfsJ4Li0KqvC0KFDY8KECSs/o1degd12g3ff7XqwZmZFeugh2HvvLk0qaWJEDK02rLt3SO+dvgUOSV+S9FNJH+5OmalhwIyIeCkiWoAxwBEZlLui0aOdGMysse2zTy7Fdvc1oVcBgyUNBr4P/Aa4HvhkN8vdDHi17PssYM/yESSNAEYADBw4kPHjx6/0TNZed10/68PMGl5X9n+d6W5yaI2IkHQE8MuIuFbSyVkE1pmIuJrk0eEMHTo0hg8fvvKFDB8On/88zJkDRx0F06Yl/QcPhuuvh9VXBwkWLYLVVoOdd4b+/eHRR2GNNWDcOBgxAn75S9h332Tc5uZkvJJrr4WPfxyeeAJOPBHOPRdOOCEp85VX4OCDkzJOPx3mz4fp0+GLX4Tzz4djj4U33oCNN4aWliSelhYYNAi23RZuvBH69UtiKx/e1AS77LLisk6Zsjy+F16Az3426f/oozwxZQp77L47rLkmfOQjSf/Jk5Nympth4cJkHq2t0LdvMnzJkmS53n0Xpk5Nyo5Yvq5aWpLvkJQ7bx6svTYsXgzPPZcs249+BF/4QtLvYx9Lxn322aSslpYkhi9/GX74w2T81OOPP86wPfdM4lprLRg5Em65BZ55BoYNS/oDjBoFRx+dxL1kSVLuvHmwySbJ9498BLbcMlmPa60FZ50FY8fChAnJsre2wrrrwj/+kfzfm5th6dIktrXXTuYzfz6st17ytzRNv35wzz1w2mlMPfNMdjrmGBgwAD74IFl/H3yQlDdp0vLlmjw5ia1fv2R5b7sNNtgA3nkHnn46iXeNNZav2+bmpNt+++X/4yOPhJdfhiefTNbFAw/AqadW3/ZHj4Zdd03+N/PnJ3E1NzPh/vsZuueeybzmzk3Wy/z5yXh77JF8njYtGdbSAuusk8S2//7w9tvJMCn5n5a2l6am5HdwySXL57/PPnDFFclvreSJJ5L5lbbT1tZke9pjj+TztGnJ+o+AxYuZ8OijDP34x5NlufTSpIzStigl67m5Ofm8evpGgUWLoE+f5Z/79k3+rrlmMh4k28wzzyRd377w3nvJ+ujTZ/k23dqa/P/79Uvm0adP0q+lJSmvX7/l39dZB/75T/42fTp7DhmSrK8114QFC5ZPu3Bhsmz9+yfTRyTDFixI5h2RrMfFi5Np03VAUxPstBPDm3JoPo6ILnfA/SSvCp0ObExSTfVsd8pMy90LGFv2/Wzg7PbGHzJkSHTbkiURixcn3ZIl1cdpba0c1tJSdbzxY8dGLFq0Yv/FiyvHbWmJWLq08zI7i6PaOK2tEfPnV5/vwoXJ8IgYN25c5XS1WLKk9nHbaruM7c23yrpYId6IZP2VlrH0f+xsHUZU/q+XLq1tulq1tFTG2taCBZX//1Ictazf0norbUel5S+ZN2/5+vjgg+RvB8vYYbwdbRu1bDctLct/Y6VlLv0Pqm2jJe2shxVinTu369titfl1FE8XdbotFACYEO3sV7t75vBF4Hjg5Ih4Q9KWwGXdLBOSBujtJW0NzAaOTeeTn6ampOtIc3Nlv9WqvA21uZlYffXlRyslfaqs7mrTV+vXWRztjdOvX/XhpTOArpRd0p2jlbbL2N58O1sXkBzxldZtLf/Hkrb/D6m2+dWqlrLWXLOy38rEUVpvpb/Sisu/1lorF08t81rZYR3Nv/z/1p5a/p/9230p5cpbmW1oFdbd5HBaRJxZ+hIRr0ga1M0yiYhWSaeSPAK8GbguIiZ3t1wzM6tNd9Njtbe+HdzNMgGIiD9HxA4RsW1EXJBFmWZmVpuuvs/h28B3gG0kPVM2aADg9zqYmTW4rlYr3QjcRfJOh/K7l+dGxJxuR2VmZoXqanJoBt4HTmk7QNL6ThBmZo2tq8lhIlB67obaDAtgmy5HZGZmhevq+xy2zjoQMzOrH929lBVJhwP7pl/HR8Qd3S3TzMyK1d0H710MfBeYknbflXRhFoGZmVlxunvmcAiwW0QsBZD0e+BJ4AfdDczMzIqTxT3i65Z9XieD8szMrGDdPXO4CHhS0jiSq5b2ZcX7HszMrAF19Q7pK4AbI+ImSeOBPdJBZ0bEG1kFZ2ZmxejqmcN04HJJmwA3AzdFxJPZhWVmZkXqUptDRPwsIvYieePbO8B1kp6XdI6kHTKN0MzMely3GqQj4uWIuCQiPgYcBxwJTM0iMDMzK05373PoI+kwSTeQPIhvGvC5TCIzM7PCdLVB+tMkZwqHAI8DY4ARETE/w9jMzKwgXW2QPpvksd3fj4h3M4wHSZcBhwEtwIvASRHxzyznYWZmHetqg/T+EfGbrBND6l5gl4jYleSqqLNzmIeZmXWg7t6iHRH3RERr+vUxYPMi4zEz640UEZ2PVRBJ/wf8ISJGVxk2AhgBMHDgwCFjxozp6fA6NG/ePPr37190GDVppFihseJtpFihseJtpFihPuPdb7/9JkbE0KoDI6LHO+A+4Lkq3RFl44wE/kSawDrqhgwZEvVm3LhxRYdQs0aKNaKx4m2kWCMaK95GijWiPuMFJkQ7+9Vuv8+hKyLigI6GS/oqcCjwqXQBzMysBxWSHDoi6SDgDOCTEbGg6HjMzHqjumuQBn4JDADulfSUpF8VHZCZWW9Td2cOEbFd0TGYmfV29XjmYGZmBXNyMDOzCk4OZmZWwcnBzMwqODmYmVkFJwczM6vg5GBmZhWcHMzMrIKTg5mZVXByMDOzCk4OZmZWwcnBzMwqODmYmVkFJwczM6vg5GBmZhWcHMzMrELdJgdJ35cUkj5UdCxmZr1NXSYHSVsAnwFeKToWM7PeqC6TA/CfwBlAFB2ImVlvVHfvkJZ0BDA7Ip6W1NF4I4AR6dd5kqb1RHwr4UPA20UHUaNGihUaK95GihUaK95GihXqM94PtzdAET1/cC7pPmDjKoNGAj8APhMR70maCQyNiHpboZ2SNCEihhYdRy0aKVZorHgbKVZorHgbKVZovHgLOXOIiAOq9Zf0UWBroHTWsDkwSdKwiHijB0M0M+vV6qpaKSKeBTYqfW/kMwczs0ZWrw3Sq4Kriw5gJTRSrNBY8TZSrNBY8TZSrNBg8RbS5mBmZvXNZw5mZlbBycHMzCo4OXSTpFGSZkt6Ku0OKRt2tqQZkqZJOrCs/0FpvxmSzioo7hUeT6LEz9OYnpG0e9m4X5H0Qtp9pQdjPD+N5SlJ90jatF5jTed9maTn05j+JGndsmF1tS1IOkbSZElLJQ1tM6yuYq2mnmJJ47lO0luSnivrt76ke9Nt8V5J66X9291+60pEuOtGB4wCTq/Sf2fgaaAvyeW5LwLNafcisA2wejrOzj0c8xbAWOBl4ENpv0OAuwAB/wL8Le2/PvBS+ne99PN6PRTn2mWf/w34Vb3Gms7/M0Cf9PMlwCX1ui0AOwE7AuNJrgis++22LMa6iaUspn2B3YHnyvpdCpyVfj6rbHuouv3WW+czh/wcAYyJiEUR8XdgBjAs7WZExEsR0QKMScftSdUeT3IEcH0kHgPWlbQJcCBwb0TMiYh3gXuBg3oiyIh4v+zrWmXx1l2sabz3RERr+vUxkvt0SvHW1bYQEVMjotpTBeou1irqKRYAIuIBYE6b3kcAv08//x44sqx/te23rjg5ZOPU9PTwutKpI7AZ8GrZOLPSfu317xHljydpM6he471A0qvACcCP0t51GWsbXyM5OoTGiLekEWKtp1g6MjAiXk8/vwEMTD83RPx1dRNcverkcR9XAeeTHNWeD/yEZMdQmFoeT9KzEbWvo1gj4raIGAmMlHQ2cCpwTo8G2EZn8abjjARagRt6Mra2aonVekZEhKSGum/AyaEG0c7jPtqSdA1wR/p1NkndfsnmaT866J+J9uLt6PEkHcQ7Gxjepv/4vGOt4gbgzyTJoZBYofN4JX0VOBT4VKQVzBS0LazEui1X2Ha7EjqKsZ68KWmTiHg9rTZ6K+3fGPEX3ejR6B2wSdnn00jqawEGsWLD3kskDWl90s9bs7wxbVBBsc9keYP0Z1mxkezxtP/6wN9JGnjXSz+v30PxbV/2+V+B/63XWNP5HwRMATZs079utwUqG6TrNtayGOsmljZxbcWKDdKXsWKD9KUdbb/11hUeQKN3wH8DzwLPALe3SRYjSa6qmAYcXNb/EGB6OmxkgbGXJwcBV6QxPdtmh/E1kobJGcBJPRjfLcBz6br9P2Czeo01nfcMkrrkp9LuV/W6LQBHkdR1LwLeBMbWa6ztxF83saTx3AS8DixO1+vJwAbAX4AXgPtID1Q62n7rqfPjM8zMrIKvVjIzswpODmZmVsHJwczMKjg5mJlZBScHMzOr4ORgvZ6kDcqeqvtG2VN250m6Mqd5/rukE7sw3eqSHpDkG1gtV76U1ayMpFHAvIi4PMd59AEmAbvH8gf1rcz055A8eK7Qx3PYqs1nDmbtkDRc0h3p51GSfi/pQUkvS/qcpEslPSvpbkmrpeMNkXS/pImSxrbztM39gUmlxCBpfOmdCpI+JGlm+nmQpMfTs5hnJG2fTn8ryYMIzXLj5GBWu21JduyHA6OBcRHxUeAD4LNpgvgFcHREDAGuAy6oUs7ewMQa5vct4GcRsRswlOTOW0juGt+jG8th1inXW5rV7q6IWCzpWZLnDd2d9n+W5Lk6OwK7APemDzZsJnmkQlubAFNrmN+jJE+k3Rz4Y0S8ABARSyS1SBoQEXO7s0Bm7XFyMKvdIoCIWCppcSxvsFtK8lsSMDki9uqknA+ANdr0U/p3tVKPiLhR0t9IHtT2Z0nfjIi/poP7Agu7vihmHXO1kll2pgEbStoLQNJqkgZVGW8qsF2bfqVqouEkZxxI2gZ4KSJ+DtwG7Jr23wB4OyIWZ74EZiknB7OMRPLKyqOBSyQ9TfJk1o9XGfUukncOlztA0hPAAcAcSf8GfAF4TtJTJNVV16fj7gfcmfkCmJXxpaxmBZD0J+CMiHhB0njg9IiYUOO0fyR5T8D0PGO03s1nDmbFOIukYXqlSFoduNWJwfLmMwczM6vgMwczM6vg5GBmZhWcHMzMrIKTg5mZVXByMDOzCv8f9zHNnIzEaj8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch = w.channels[0]\n",
    "plt.subplot(211)\n",
    "plt.plot(ch.times*1e6, ch.volts, color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.ylim(-10,10)\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "ch = w.channels[1]\n",
    "plt.subplot(212)\n",
    "plt.plot(ch.times*1e6, ch.volts, color='red')\n",
    "plt.xlabel(\"Time (µs)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.ylim(-4,4)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS2000-B\n",
    "\n",
    "<img src=\"https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2000-B.png\" width=\"40%\">\n",
    "\n",
    "\n",
    "### First the `.csv` data\n",
    "\n",
    "Let's look at what the accompanying `.csv` data looks like.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"DS2000-B.csv\"\n",
    "\n",
    "# unfortunately this csv file was generated by RigolWFM and by not the scope itself\n",
    "csv_data = np.genfromtxt(repo+filename, delimiter=',', skip_header=2).T\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(csv_data[0]*1e6,csv_data[1], color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.ylim(-10,10)\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(csv_data[0]*1e6,csv_data[2], color='red')\n",
    "plt.xlabel(\"Time (µs)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.grid(True)\n",
    "plt.ylim(-2,2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now for the `wfm` data\n",
    "\n",
    "First let's have look at the description of the internal file structure. We see that only channel 1 has been enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading 'https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2000-B.wfm?raw=true'\n",
      "    General:\n",
      "        File Model   = wfm2000\n",
      "        User Model   = DS2000\n",
      "        Parser Model = wfm2000\n",
      "        Firmware     = 00.03.00.01.03\n",
      "        Filename     = DS2000-B.wfm\n",
      "        Channels     = [1, 2]\n",
      "\n",
      "     Channel 1:\n",
      "         Coupling =       DC\n",
      "            Scale =     5.00  V/div\n",
      "           Offset =    -5.00  V\n",
      "            Probe =      10X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =   50.000 µs/div\n",
      "           Offset = -199.200 µs\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =   700000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...    699999,   700000]\n",
      "           Raw    = [      103,      102,      103  ...       103,      103]\n",
      "           Times  = [-549.200 µs,-549.199 µs,-549.198 µs  ... 150.799 µs,150.800 µs]\n",
      "           Volts  = [200.00 mV,  0.00  V,200.00 mV  ... 200.00 mV,200.00 mV]\n",
      "\n",
      "     Channel 2:\n",
      "         Coupling =       DC\n",
      "            Scale =     1.00  V/div\n",
      "           Offset =    -3.00  V\n",
      "            Probe =      10X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =   50.000 µs/div\n",
      "           Offset = -199.200 µs\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =   700000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...    699999,   700000]\n",
      "           Raw    = [       71,       72,       71  ...        71,       72]\n",
      "           Times  = [-549.200 µs,-549.199 µs,-549.198 µs  ... 150.799 µs,150.800 µs]\n",
      "           Volts  = [760.00 mV,800.00 mV,760.00 mV  ... 760.00 mV,800.00 mV]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# raw=true is needed because this is a binary file\n",
    "wfm_url = repo + \"DS2000-B.wfm\" + \"?raw=true\"  \n",
    "\n",
    "w = rigol.Wfm.from_url(wfm_url, 'DS2000')\n",
    "description = w.describe()\n",
    "print(description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch = w.channels[0]\n",
    "plt.subplot(211)\n",
    "plt.plot(ch.times*1e6, ch.volts, color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.ylim(-10,10)\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "ch = w.channels[1]\n",
    "plt.subplot(212)\n",
    "plt.plot(ch.times*1e6, ch.volts, color='red')\n",
    "plt.xlabel(\"Time (µs)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.ylim(-2,2)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS2072A-1\n",
    "\n",
    "### Look at a screen shot\n",
    "\n",
    "Start with a `.wfm` file from a Rigol DS2072 scope.  Evidently the image capture on these scopes is buggy and shows no traces.  However, it does show the time scale and that both channels were active\n",
    "\n",
    "<img src=\"https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2072A-1.png\" width=\"40%\">\n",
    "\n",
    "### Look at the data in the `.csv` file\n",
    "\n",
    "First let's look at plot of the data from the corresponding `.csv` file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"DS2072A-1.csv\"\n",
    "\n",
    "csv_data = read_rigol_csv(repo+filename)\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(csv_data[0]*1e3,csv_data[1], color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(csv_data[0]*1e3,csv_data[2], color='red')\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now for the `.wfm` data\n",
    "\n",
    "First a textual description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading 'https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2072A-1.wfm?raw=true'\n",
      "    General:\n",
      "        File Model   = wfm2000\n",
      "        User Model   = 2000\n",
      "        Parser Model = wfm2000\n",
      "        Firmware     = 00.03.05.03.03\n",
      "        Filename     = DS2072A-1.wfm\n",
      "        Channels     = [1, 2]\n",
      "\n",
      "     Channel 1:\n",
      "         Coupling =       DC\n",
      "            Scale =   200.00 mV/div\n",
      "           Offset =   336.00 mV\n",
      "            Probe =    0.01X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =  500.000 µs/div\n",
      "           Offset =    0.000  s\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =  7000000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...   6999999,  7000000]\n",
      "           Raw    = [      207,      207,      208  ...       208,      207]\n",
      "           Times  = [-3.500 ms,-3.500 ms,-3.500 ms  ...  3.500 ms, 3.500 ms]\n",
      "           Volts  = [304.00 mV,304.00 mV,312.00 mV  ... 312.00 mV,304.00 mV]\n",
      "\n",
      "     Channel 2:\n",
      "         Coupling =       DC\n",
      "            Scale =   200.00 mV/div\n",
      "           Offset =  -412.00 mV\n",
      "            Probe =    0.01X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =  500.000 µs/div\n",
      "           Offset =    0.000  s\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =  7000000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...   6999999,  7000000]\n",
      "           Raw    = [      113,      114,      113  ...       113,      114]\n",
      "           Times  = [-3.500 ms,-3.500 ms,-3.500 ms  ...  3.500 ms, 3.500 ms]\n",
      "           Volts  = [300.00 mV,308.00 mV,300.00 mV  ... 300.00 mV,308.00 mV]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# raw=true is needed because this is a binary file\n",
    "wfm_url = repo + \"DS2072A-1.wfm\" + \"?raw=true\"  \n",
    "w = rigol.Wfm.from_url(wfm_url, '2000')\n",
    "\n",
    "description = w.describe()\n",
    "print(description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch = w.channels[0]\n",
    "plt.subplot(211)\n",
    "plt.plot(ch.times*1e6, ch.volts, color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "ch = w.channels[1]\n",
    "plt.subplot(212)\n",
    "plt.plot(ch.times*1e6, ch.volts, color='red')\n",
    "plt.xlabel(\"Time (µs)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS2072A-3\n",
    "\n",
    "<img src=\"https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2072A-3.png\" width=\"40%\">\n",
    "\n",
    "\n",
    "### First the `.csv` data\n",
    "\n",
    "Let's look at what the accompanying `.csv` data looks like.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"DS2072A-3.csv\"\n",
    "csv_data = read_rigol_csv(repo+filename)\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(csv_data[0]*1e3,csv_data[1], color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "#plt.ylim(-10,10)\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(csv_data[0]*1e3,csv_data[2], color='red')\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.grid(True)\n",
    "#plt.ylim(-2,2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now for the `wfm` data\n",
    "\n",
    "First let's have look at the description of the internal file structure. We see that only channel 1 has been enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading 'https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS2072A-3.wfm?raw=true'\n",
      "    General:\n",
      "        File Model   = wfm2000\n",
      "        User Model   = DS2000\n",
      "        Parser Model = wfm2000\n",
      "        Firmware     = 00.03.05.03.03\n",
      "        Filename     = DS2072A-3.wfm\n",
      "        Channels     = [1, 2]\n",
      "\n",
      "     Channel 1:\n",
      "         Coupling =       DC\n",
      "            Scale =   100.00 mV/div\n",
      "           Offset =    60.00 mV\n",
      "            Probe =    0.01X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =  500.000 µs/div\n",
      "           Offset =    0.000  s\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =  7000000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...   6999999,  7000000]\n",
      "           Raw    = [      218,      218,      218  ...       218,      218]\n",
      "           Times  = [-3.500 ms,-3.500 ms,-3.500 ms  ...  3.500 ms, 3.500 ms]\n",
      "           Volts  = [304.00 mV,304.00 mV,304.00 mV  ... 304.00 mV,304.00 mV]\n",
      "\n",
      "     Channel 2:\n",
      "         Coupling =       DC\n",
      "            Scale =   100.00 mV/div\n",
      "           Offset =  -302.00 mV\n",
      "            Probe =    0.01X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =  500.000 µs/div\n",
      "           Offset =    0.000  s\n",
      "            Delta =    1.000 ns/point\n",
      "           Points =  7000000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...   6999999,  7000000]\n",
      "           Raw    = [      127,      126,      128  ...       127,      127]\n",
      "           Times  = [-3.500 ms,-3.500 ms,-3.500 ms  ...  3.500 ms, 3.500 ms]\n",
      "           Volts  = [302.00 mV,298.00 mV,306.00 mV  ... 302.00 mV,302.00 mV]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# raw=true is needed because this is a binary file\n",
    "wfm_url = repo + \"DS2072A-3.wfm\" + \"?raw=true\"  \n",
    "\n",
    "w = rigol.Wfm.from_url(wfm_url, 'DS2000')\n",
    "description = w.describe()\n",
    "print(description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch = w.channels[0]\n",
    "plt.subplot(211)\n",
    "plt.plot(ch.times*1e3, ch.volts, color='green')\n",
    "plt.title(filename + \" (CH1 Top, CH2 Bottom)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "#plt.ylim(-10,10)\n",
    "plt.grid(True)\n",
    "plt.xticks([])\n",
    "\n",
    "ch = w.channels[1]\n",
    "plt.subplot(212)\n",
    "plt.plot(ch.times*1e3, ch.volts, color='red')\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "#plt.ylim(-2,2)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "nteract": {
   "version": "0.15.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
